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How to use name_scheme method in avocado

Best Python code snippet using avocado_python

variables_baptism.py

Source:variables_baptism.py

...39    if list_of_names is None:40        list_of_names = [prop_obj.lean_data["name"] for prop_obj in PO.ProofStatePO.context_dict.values()]41    good_name = None42    if not hasattr(math_type, name_scheme):43        init_name_scheme(math_type, list_of_names)44    while good_name is None:45        for name in math_type.name_scheme:46            if name not in list_of_names:47                good_name = name48            math_type.name_scheme.remove(name)49        init_name_scheme(math_type, list_of_names)50    return good_name51def init_name_scheme(math_type: PO.PropObj, list_of_names: list, name_prescheme=None):52    """53    determine a list of variable names and attribute it to math_type.name_scheme54    the main sheme is the following:55    first determine the base letter, and second add a decoration (prime or index or nothing)56    - for elements (ie the type of math_type is "Type"), if there is a hint_set which is an upper case letter,57    the base letter is the corresponding lower case letter58    if there is no variable of type math_type, then choose the name according to59    :param math_type: a mathematical type60    :param list_of_names: the list of all current variables61    :param name_prescheme: a hint for naming new variables62    name_prescheme, if not None, is a list of strings of one of the following form:63        - a letter of the latin alphabet, in lower or upper case ;64        - a letter followed by "-", meaning all letters starting from the given one in the alphabetic order65        - an interval of letters, e.g. "[a-e]" or "[A-E]"...

gpu_lists.py

Source:gpu_lists.py

1def get_nvidia_gtx_gpu():2    name_scheme = {"prefix": "gtx",3                   "generation_max": 10,4                   "perf_tier_max": 8,5                   "revision": 0,6                   "suffix": "Ti", }7    gpus = []8    listing = True9    generation = 510    while listing:11        if generation == (name_scheme["generation_max"]+6):12            listing = False1314        perf_tier = 515        while perf_tier <= name_scheme["perf_tier_max"]:16            if generation == 8:17                break18            elif generation == 16:19                if perf_tier > 6:20                    break2122            suffix = 023            series = "{} {}{}{}".format(name_scheme["prefix"], generation, perf_tier, name_scheme["revision"])24            gpus.append(series)25            suffix += 1 26            if suffix == 1:27                series = "{} {}{}{} {}".format(name_scheme["prefix"], generation, perf_tier, name_scheme["revision"], name_scheme["suffix"])28                gpus.append(series)29            perf_tier += 130        generation += 131        if generation > name_scheme["generation_max"]:32            generation = 163334    return gpus353637def get_nvidia_rtx_gpu():38    name_scheme = {"prefix": "rtx",39                    "generation_min": 20,40                   "generation_max": 30,41                   "perf_tier_min": 6,42                   "perf_tier_max": 9,                   43                   "revision": 0,44                   "suffix": "Ti"}45    gpus = []46    listing = True47    generation = name_scheme["generation_min"]48    while listing:49        perf_tier = name_scheme["perf_tier_min"]5051        while perf_tier <= name_scheme["perf_tier_max"]:52            53            if perf_tier == name_scheme["perf_tier_max"] and generation == name_scheme["generation_min"]:54                break55            56            if perf_tier == name_scheme["perf_tier_max"]:57                series = "{} {}{}{}".format(name_scheme["prefix"], generation, perf_tier, name_scheme["revision"])58                gpus.append(series)59            else:60                series = "{} {}{}{}".format(name_scheme["prefix"], generation, perf_tier, name_scheme["revision"])61                gpus.append(series)62                series = "{} {}{}{} {}".format(name_scheme["prefix"], generation, perf_tier, name_scheme["revision"], name_scheme["suffix"])63                gpus.append(series)64            perf_tier += 16566        generation += 106768        if generation > name_scheme["generation_max"]:69            listing = False7071    return gpus727374def get_amd_gpu():75    amd = [76        "rx 460","rx 470", "rx 480", 77        "rx 550", "rx 560", "rx 570", "rx 580",78        "rx 5500", "rx 5500 xt", "rx 5600", "rx 5600 xt", "rx 5700", "rx 5700 xt",79        "rx 6600", "rx 6600 xt", "rx 6700 xt", "rx 6800", "rx 6800 xt", "rx 6900 xt" 80        ]81        82    return amd83            84858687if __name__ == '__main__':88    gpu_lists = []89    nvidia_gtx = get_nvidia_gtx_gpu()90    nvidia_rtx = get_nvidia_rtx_gpu()91
...

solver.py

Source:solver.py

1import numpy as np2import matplotlib.pyplot as plt3from .utils import create_dir4class ODESim:5    def __init__(self, times, schemes, model, init_value, fig_dir=None):6        # Time related variables7        self.times = times8        self.ntimes = len(times)9        self.dt = times[1] - times[0]10        # Model and schemes11        self.model = model12        self.schemes = schemes13        self.nschemes = len(schemes)14        # variable of interest15        self.v = np.zeros((self.nschemes, self.ntimes, self.model.nd))16        self.v0 = init_value17        # Figures directory18        if not fig_dir is None:19            self.fig_dir = f'figures/{fig_dir}/'20            create_dir(self.fig_dir)21    22    def forwardEuler(self, v):23        """ Use model to apply forward Euler scheme """24        v[0] = self.v025        for i in range(1, self.ntimes):26            v[i] = v[i - 1] + self.dt * self.model.f(v[i - 1], self.times[i - 1])27    28    def midpoint(self, v):29        """ Use model to apply midpoint formula """30        v[0] = self.v031        v[1] = v[0] + self.dt * self.model.f(v[0], self.times[0])32        for i in range(2, self.ntimes):33            v[i] = v[i - 2] + 2 * self.dt * self.model.f(v[i - 1], self.times[i - 1])34    35    def multi_step2(self, v):36        """ Most accurate explicit 2multistep method """37        v[0] = self.v038        v[1] = v[0] + self.dt * self.model.f(v[0], self.times[0])39        for i in range(2, self.ntimes):40            v[i] = - 4 * v[i - 1] + 5 * v[i - 1] + self.dt * \41                        (4 * self.model.f(v[i - 1], self.times[i - 1]) + 2 * self.model.f(v[i - 2], self.times[i - 2]))42    43    def backwardEuler(self, v):44        """ Only works with stiff problem for now """45        v[0] = self.v046        for i in range(1, self.ntimes):47            v[i] = self.model.fbackwardEuler(v[i - 1], self.times[i - 1], self.dt)48        49    def trapezoidal(self, v):50        """ Only works with nonlinear problem for now """51        v[0] = self.v052        for i in range(1, self.ntimes):53            v[i] = self.model.ftrapez(v[i - 1], self.times[i - 1], self.dt)54        print(v.shape)55    56    def run_schemes(self):57        """ Apply scheme and plot the results """58        for i_scheme, name_scheme in enumerate(self.schemes):59            scheme = getattr(self, name_scheme)60            scheme(self.v[i_scheme, :])61    62    def plot(self, figname=None):63        for i_scheme, name_scheme in enumerate(self.schemes):64            if figname is None:65                self.model.plot(self.times, self.v[i_scheme, :], f'{name_scheme} - dt = {self.dt:.2e}', self.fig_dir + name_scheme)66            else:...

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